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Perturbation of image-based Malware Detection with Smali level morphing techniques

Perturbation of Image-based Malware Detection with Smali lev...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Federico Gerardi Giacomo Iadarola Fabio Martinelli Antonella Santone Francesco Mercaldo Geckosoft Pisa Italy Institute of Informatics and Telematics National Research Council of Italy (CNR) Pisa Italy University of Molise Campobasso Italy

Considering the weaknesses of signature-based approaches adopted by current antimalware, from both academic and industrial side there is a boost in the development of techniques exploiting artificial intelligence, where one of the most promising are based on the representation of application under analysis as image. In order to understand whether these approaches can be effectively adopted in the real-world, starting from a detector based on deep learning, in this paper we evaluate the resilience of these approaches when morphed samples are considered. We present DexWave, a tool aimed to automatically inject perturbations techniques targeting the smali code representation of Android applications. The experimental analysis demonstrate that image-based malware classifier are vulnerable to simple perturbations attack.

关键词： Deep learning Codes Perturbation methods Detectors Malware Artificial intelligence Resilience

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Domain Adaptive Semantic Segmentation via image Translation and Representation Alignment

Domain Adaptive Semantic Segmentation via Image Translation ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Jingxuan Kang Bin Zang Weipeng Cao School of Computer Science University of Liverpool Liverpool UK School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixel-centroid triplet loss is designed to explicitly minimize the intra-class feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 → Cityscapes and SYNTHIA → Cityscapes scenarios.

关键词： image segmentation Adaptation models Semantics Pipelines Predictive models Linear programming Robustness

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Camouflaged bot detection using the friend list

Camouflaged bot detection using the friend list

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Maxim Kolomeets Olga Tushkanova Dmitry Levshun Andrey Chechulin Laboratory of Computer Security Problems St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) 14th line of VO 39 St. Petersburg Russia ITMO University Kronverksky Pr. 49 St. Petersburg Russia Université Paul Sabatier Toulouse III 118 Route de Narbonne Toulouse France

The paper considers the task of bot detection in social networks. Study is focused on the case when the account is closed by the privacy settings, and the bot needs to be identified by the friend list. The paper proposes a solution that is based on machine learning and statistical methods. Social network VKontakte is used as a data source. The paper provides the review of data, that one needs to get from the social network for bot detection in the case when the profile is closed by privacy settings. The paper includes a description of features extraction from VKontakte social network and extracting complexity evaluation; description of features construction using statistics, Benford's law and Gini index. The paper describes the experiment. To collect data for training, we collect bots and real users datasets. To collect bots we made fake groups and bought bots of different quality for them from 3 different companies. We performed two series of experiments. In the first series, all the features were used to train and evaluate the classifiers. In the second series of experiments, the features were preliminary examined for the presence of strong correlation between them. The results demonstrated the feasibility of rather highaccuracy private account bot detection by means of unsophisticated off-the-shelf algorithms in combination with data scaling. The Random Forest Classifier yields the best results, with an ROC AUC more than 0.9 and FPR less than 0.3. In paper we also discuss the limitations of the experimental part, and plans for future research.

关键词： Training Bot (Internet) Privacy Social networking (online) Statistical analysis Software algorithms Feature extraction

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Fine-Grain Level Sports Video Search Engine 26th

Fine-Grain Level Sports Video Search Engine

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26th International conference on MultiMedia Modeling (MMM)

作者： Song, Zikai Yu, Junqing Cai, Hengyou Hu, Yangliu Chen, Yi-Ping Phoebe Huazhong Univ Sci & Technol Ctr Network & Computat Sch Comp Sci & Technol Wuhan Peoples R China La Trobe Univ Melbourne Vic 3086 Australia

ISBN: (纸本)9783030377311;9783030377304

It becomes an urgent demand how to make people find relevant video content of interest from massive sports videos. We have designed and developed a sports video search engine based on distributed architecture, which aims to provide users with content-based video analysis and retrieval services. In sports video search engine, we focus on event detection, highlights analysis and image retrieval. Our work has several advantages: (I) CNN and RNN are used to extract features and integrate dynamic information and a new sliding window model are used for multi-length event detection. (II) For highlights analysis. An improved method based on self-adapting dual threshold and dominant color percentage are used to detect the shot boundary. Affect arousal method are used for highlights extraction. (iii) For image's indexing and retrieval. Hyperspherical soft assignment method is proposed to generate image descriptor. Enhanced residual vector quantization is presented to construct multi-inverted index. Two adaptive retrieval methods based on hype-spherical filtration are used to improve the time efficient. (IV) All of previous algorithms are implemented in the distributed platform which we develop for massive video data processing.

关键词： Video search engine Event detection Highlights analysis image retrieval

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Object Pose Estimation for Robotic Grasping based on Multi-view Keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots image color analysis Robot kinematics Pose estimation Robot vision systems

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IO Challenges for Human Brain Atlasing using Deep Learning methods - An in-depth analysis 27

IO Challenges for Human Brain Atlasing using Deep Learning M...

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27th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Oden, Lena Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Pleiter, Dirk Forschungszentrum Julich JSC Julich Germany Forschungszentrum Julich Inst Neurosci & Med INM 1 Julich Germany Fernuniv Fak Math & Informat Hagen Germany

ISBN: (纸本)9781728116440

The use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. These applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. These leads to a decrease of the total runtime of up to 80%.

关键词： Training Task analysis Deep learning Graphics processing units Brain Bandwidth image resolution

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Norm-Aware Embedding for Efficient Person Search

Norm-Aware Embedding for Efficient Person Search

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Di Zhang, Shanshan Yang, Jian Schiele, Bernt Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ PCA LabKey Lab Intelligent Percept & Syst High D Nanjing Peoples R China Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9781728171685

Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, we present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by misalignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, our method is easy to train and resource-friendly, running at 12 fps on a single GPU.

关键词： Embeddings

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parallel coordinates visualization in the ELK stack 30

Parallel coordinates visualization in the ELK stack

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30th International conference on Computer Graphics and Machine Vision, GraphiCon 2020

作者： Galkin, Timofei Grigorieva, Maria National Research Nuclear University "MEPhI" Kashirskoe shosse 31 Moscow115409 Russia Scientific Research Computing Center Lomonosov Moscow State University Leninskie Gory 1 p.4 Moscow119991 Russia Moscow Center of Fundamental and Applied Mathematics Leninskie Gory 1 Moscow119991 Russia

Modern large-scale distributed computing systems, processing large volumes of data, require mature monitoring systems able to control and track in resources, networks, computing tasks, queues and other components. In recent years, the ELK stack has become very popular for the monitoring of computing environment, largely due to the efficiency and flexibility of the ElasticSearch storage and wide variety of Kibana visualization tools. The analysis of computing infrastructure metadata often requires the visual exploration of multiple parameters simultaneously on one graphical image. Stacked bar charts, heatmaps, radar charts are widely used for the multivariate visual data analysis, but these methods have limitations on the number of parameters. In this research the authors propose to enhance the capacity of Kibana, adding parallel Coordinates diagram - one of the most powerful method for visual interactive analysis of high-dimensional data. It allows to compare many variables together and observe correlations between them. This work describes the development process of parallel Coordinates as a Kibana plugin, and demonstrates an example of visual data analysis based on the Nginx logs metadata. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visualization

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DeepPFCN: Deep parallel Feature Consensus Network for Person Re-identification 7th

DeepPFCN: Deep Parallel Feature Consensus Network for Person...

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7th National conference on Computer Vision, Pattern Recognition, image processing, and Graphics, NCVPRIPG 2019

作者： Singh, Shubham Kumar Miyapuram, Krishna P. Raman, Shanmuganathan Indian Institute of Technology Gandhinagar PalajGujarat382355 India

ISBN: (纸本)9789811586965

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Optimizing Algorithms for low CPU Usage in Different Scenarios 20

Optimizing Algorithms for low CPU Usage in Different Scenari...

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20th International Carpathian Control conference (ICCC)

作者： Popa, Bogdan Popescu, Dan Roman, Monica Constantinescu, Radu Lucian Univ Craiova Dept Automat Control & Elect Craiova Romania

ISBN: (纸本)9781728107011

This paper proposes a series of results over the image processing algorithms and for the shortest path algorithm that influence the percentage of CPU utilization. Starting with the fractal generation to image analysis, compression for big images and also any algorithm, the parallel programing is today one of the most important factor for low consumption and good power balanced for the processing unit. A good balance between the processing capacity, the algorithm and the time of processing is hard to obtain due to the number of calculus. In this work a series of results and comparison between sequential and parallel algorithms are presented. Also, there are presented test scenarios in different cases that confirm or infirm the parallel strategy chosen. This paper will integrate the results obtained with the proposed algorithms reports or comparisons of the proposed parallelization methods and will provide conclusions and ideas for future research in this field with extensive uses.

关键词： parallel programming image processing speed-up factor processor usage

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